JP2013154037A - Surgical support device, surgical support method and surgical support program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To present a more appropriate excision method in view of the position of an abnormal area in an organ and the positional relationship among the target organ and the surrounding organs in a surgical support device which presents the area to be excised including the abnormal area in the organ.SOLUTION: This surgical support device comprises: a medical image-capturing means 11 for capturing a three-dimensional medical image of a subject; a target tissue area-extracting means 12 for extracting the area of the target tissue from the three-dimensional medical image having been captured by the medical image-capturing means 11; an abnormal area-extracting means 13 for extracting an abnormal area from the target tissue area having been extracted by the target tissue area-extracting means 12; an excision method-acquiring means 16 for acquiring a plurality of excision methods in which the area to be excised is set up satisfying respective requirements for determining the area to be excised relative to the abnormal area on the basis of a plurality of conditions for determining the area to be excised; and an excision method-presenting means 17 for presenting the plurality of excision methods having been acquired by the excision method-acquiring means.

Description

  The present invention relates to a surgical operation support apparatus, method, and program for presenting an ablation region including an abnormal region in an organ such as a liver or lung.

  When performing an operation to remove an abnormal region in an organ such as the liver or lung, a three-dimensional medical image representing the shape of the target organ by performing, for example, CT (Computed Tomography) or MRI (Magnetic Resonance Imaging) before surgery. And the ablation area is determined in advance based on the three-dimensional medical image.

  As a method for determining the ablation region based on the three-dimensional medical image, for example, blood vessels and bronchi are extracted from the three-dimensional medical image, and some of these regions related to the abnormal region are oxygen or A method has been proposed in which a region for supplying nutrients is extracted as a dominant region, and this dominant region is determined as an ablation region.

  On the other hand, for an abnormal region near the surface of the target organ, a surgical technique has been proposed in which partial excision is performed so as not to damage blood vessels or bronchi (see, for example, Non-Patent Document 1). Based on the lesion area of the liver and the multiple vessels running through the liver, the area is expanded in a spherical shape until it touches one of the multiple vessels outward from the center or center of gravity of the lesion area, There has been proposed a method for determining a substantially conical region in contact with a spherical region as an ablation region.

Patent No. 4193201

Makuuchi, M., Kosuge, T., Takayama, T, et al. “Surgery for amall liver cancers.” Smein. Surg. Oncol., 9.4, 298-304, 1993.

  Here, it is preferable that the resection area is appropriately determined so that the burden on the patient and the operation time can be reduced. For example, the ablation area should preferably have as little surface area as possible for the cut surface of the ablation area in order to reduce the patient burden and operation time due to surgery, and the volume of the ablation area should be as small as possible to leave as much of the patient's healthy organ as possible. Is preferred.

  From this point of view, for example, an ablation region is set so that the surface area of the cut surface of the ablation region is minimized, and one ablation method is determined, or an ablation region where the volume of the ablation region is minimized is set. Thus, it may be possible to determine one excision method.

  However, when one excision method is determined so as to satisfy one specific condition in this way, when an operation is actually performed, for example, when an abnormal region exists on the back side, the target organ In some cases, the excision method determined as described above may be difficult to excise based on the position of the abnormal region and the positional relationship between the target organ and surrounding organs.

  In view of the above circumstances, the present invention provides a surgery support apparatus, method, and program capable of presenting a more appropriate excision method that takes into account the position of an abnormal region in a target organ and the positional relationship between the target organ and its surrounding organs. It is intended to do.

  The surgery support apparatus of the present invention includes a medical image acquisition unit that acquires a three-dimensional medical image of a subject, a target tissue region extraction unit that extracts a target tissue region from the three-dimensional medical image acquired by the medical image acquisition unit, and An abnormal region extracting unit that extracts an abnormal region from the target tissue region extracted by the target tissue region extracting unit, and an ablation region that satisfies each ablation region determination condition for the abnormal region based on a plurality of ablation region determination conditions An ablation method acquisition unit that acquires a plurality of ablation methods that have been set and an ablation method presentation unit that presents a plurality of ablation methods acquired by the ablation method acquisition unit.

  In the surgery support apparatus of the present invention, a minimum inclusion body that contains the abnormal region extracted by the abnormal region extraction unit is set, and the region including the set inclusion body and a point on the surface of the target tissue A partial ablation region extracting means for extracting a region as a partial ablation region, and extracting a region of the tubular structure from the three-dimensional medical image and using a region including a part of the region of the tubular structure related to the abnormal region and the abnormal region as a control region A control region extracting means for extracting is further provided, and the excision method acquiring means is configured to acquire either the partial excision region or the control region with respect to the abnormal region and acquiring the excision method.

  In addition, the ablation region determination condition for setting either the partial ablation region or the dominant region can be the distance between the abnormal region and the surface of the target tissue.

  In addition, one of the plurality of ablation region determination conditions can be the volume of the ablation region.

  One of the plurality of ablation region determination conditions can be the surface area of the cut surface of the ablation region.

  Further, the excision method acquisition means can accept an instruction to correct the acquired excision method, and the excision method presentation means can present an amendment method after correction based on the correction instruction.

  Further, the excision method acquisition means accepts an instruction to move the predetermined excision region to another excision method in one of the obtained plural excision methods, and the excision method presentation means changes to the movement instruction. Based on this, the excision method after moving the predetermined excision region can be presented.

  Further, the excision method acquisition means can acquire an evaluation value corresponding to each ablation region determination condition, and the excision method presentation means can present the evaluation value acquired by the excision method acquisition means.

  Further, the excision method presenting means can present a list in which the excision method corresponding to each excision region determination condition is associated with the evaluation value.

  Further, the medical image acquisition means acquires a three-dimensional functional image as a three-dimensional medical image, and the excision method acquisition unit uses a plurality of excision methods based on the excision region determination condition based on the function level in the three-dimensional functional image One of them can be obtained.

  In addition, the target tissue can be a liver or a lung.

  The surgery support method of the present invention acquires a three-dimensional medical image of a subject, extracts a region of a target tissue from the acquired three-dimensional medical image, extracts an abnormal region from the extracted target tissue region, Based on the ablation region determination conditions, a plurality of ablation methods in which an ablation region satisfying each ablation region determination condition is set for an abnormal region are acquired, and the acquired ablation methods are presented.

  The surgery support program according to the present invention uses a computer to extract a target tissue region from a medical image acquisition unit that acquires a three-dimensional medical image of a subject and a three-dimensional medical image acquired by the medical image acquisition unit. Means, an abnormal area extracting means for extracting an abnormal area from the target tissue area extracted by the target tissue extracting means, and an ablation area that satisfies each ablation area determination condition for the abnormal area based on a plurality of ablation area determination conditions And ablation method acquisition means for acquiring a plurality of ablation methods acquired by the ablation method acquisition means.

  According to the surgical operation support apparatus, method and program of the present invention, based on a plurality of resection area determination conditions, obtain a plurality of resection methods in which ablation areas satisfying each resection area determination condition are set for an abnormal area, Since the obtained multiple excision methods are presented, more appropriate considering the position of the abnormal region in the target organ and the positional relationship between the target organ and the surrounding organs from the multiple excision methods presented The doctor can select the ablation method.

  Further, in the surgical operation support apparatus, method and program of the present invention, the correction instruction for the excision method acquired as described above is accepted, and the post-correction excision method based on the correction instruction can be further presented. In some cases, the options for the ablation method by the doctor can be expanded, and a more appropriate ablation method can be determined.

  In addition, an instruction to move a predetermined excision region to another excision method in one of the excision methods obtained as described above is accepted, and a predetermined excision region is determined based on the movement instruction. When the excision method after moving is presented, the excision method can be corrected by a simple method.

  In addition, when an evaluation value corresponding to each ablation region determination condition is acquired and the acquired evaluation value is presented, one of the determination materials when the doctor determines which excision method is optimal One can be presented. Further, the doctor can determine whether or not the currently determined excision method is corrected by referring to the evaluation value.

  Further, when a three-dimensional functional image is acquired as a three-dimensional medical image, and one of a plurality of ablation methods is acquired based on the ablation region determination condition based on the function level in the three-dimensional functional image. Can present an ablation method that also considers the functional level, can expand the options of the ablation method by the doctor, and can determine a more appropriate ablation method.

The block diagram which shows schematic structure of the surgery assistance system using one Embodiment of the surgery assistance apparatus of this invention. The figure which shows an example of the inclusion body set based on the abnormal area | region The figure which shows an example of a partial excision field The figure for demonstrating an example of the setting method of a control area The figure which shows an example of the display of the some cutting method The flowchart for demonstrating the effect | action of the surgery assistance system using one Embodiment of the surgery assistance apparatus of this invention. The figure for demonstrating an example of the correction method of an excision method The figure which shows the other example of the display of several cutting methods The figure which shows an example of the list which matched the excision method and evaluation value The figure which shows an example of the list | wrist which matched each cutting area | region and evaluation value in a predetermined cutting method The figure which shows an example of the list which matched each excision method, each excision area in each excision method, and an evaluation value The figure for explaining other setting methods of the inclusion body The figure for demonstrating an example of the method of determining a control area | region based on a three-dimensional functional image

  Hereinafter, an embodiment of a surgery support device, a surgery support program, and a surgery support method according to the present invention will be described in detail with reference to the drawings. FIG. 1 is a diagram illustrating a schematic configuration of a surgery support system including an embodiment of the surgery support apparatus according to the present embodiment. As shown in FIG. 1, the surgery support system according to the present embodiment includes a surgery support device 1, a storage device 2, and a display 3.

  The surgery support apparatus 1 is obtained by installing an embodiment of the surgery support program of the present invention on a single computer. The computer may be a workstation or personal computer directly operated by a doctor who performs diagnosis, or may be a server computer connected to them via a network. The surgery support program is stored in a recording medium such as a DVD or a CD-ROM, or a server computer that can be accessed from the outside connected to a network. In response to a request from a doctor, It is read from the server computer, downloaded to the computer and installed.

  The surgery support apparatus 1 includes a central processing unit (CPU) and a semiconductor memory, a hard disk in which the surgery support program described above is installed, a storage device such as an SSD (Solid State Drive), and the like. 1, a medical image acquisition unit 11 (medical image acquisition unit), a target tissue region extraction unit 12 (target tissue region extraction unit), an abnormal region extraction unit 13 (abnormal region extraction unit), and a partially excised region extraction as shown in FIG. A section 14 (partial excision area extraction means), a control area extraction section 15 (control area extraction means), an excision method acquisition section 16 (excision method acquisition means), and a display control section 17 (excision method presentation means) are configured. And each said part functions by the surgery support program installed in the hard disk being run by the central processing unit. In addition, a display 3 and an input device 4 such as a mouse are connected to the surgery support apparatus 1.

  The medical image acquisition unit 11 acquires a medical image representing a target tissue including an affected part (abnormal region) to be excised. The medical image acquisition unit 11 of the present embodiment is a three-dimensional morphological image 7 obtained by imaging the subject's liver in CT examination or MRI examination, or in a PET (Positron Emission Tomography) examination or SPECT (Single Photon Emission Computed Tomography) examination. A three-dimensional functional image 8 obtained by photographing the liver of the specimen is acquired as a medical image. The three-dimensional form image 7 and the three-dimensional function image 8 are captured in advance and stored in the storage device 2.

  The target tissue region extraction unit 12 receives the three-dimensional morphological image 7 acquired by the medical image acquisition unit 11 and extracts a liver region that is the target tissue region from the three-dimensional morphological image 7. The target tissue region extraction unit 12 according to the present embodiment calculates a feature amount that represents the outline of the liver for each voxel data value constituting the three-dimensional morphological image 7, and acquires the calculated feature amount in advance by machine learning. By evaluating based on the evaluated function, it is determined whether or not the voxel data represents the outline of the liver. By repeating this determination, voxel data representing the outline of the entire liver is extracted. In this embodiment, the Adaboost algorithm is used for obtaining the evaluation function. Various other well-known methods can be used as the liver region extraction method. For example, a machine learning method, a statistical analysis method, a linear discrimination method, a neural network, a support vector machine, or the like may be used. Good.

  The liver region extracted by the target tissue region extraction unit 12 may be displayed on the display 3, and the liver region may be corrected by the user using the input device 4.

  The abnormal region extraction unit 13 receives the organ region extracted by the target tissue region extraction unit 12 and extracts an abnormal region of the liver such as an abnormal shadow from the organ region. As an abnormal region extraction method, for example, the three-dimensional morphological image 7 may be displayed on a display, an area specified by a user's mouse or the like may be received, and the received region may be extracted as an abnormal region. You may make it extract to.

  Various known methods can be applied as a method for automatically extracting an abnormal region. Specifically, JP 2003-225231 A, JP 2003-271924 A, and “Kubota et al.,” Evaluation of lung cancer computer diagnosis support system using helical CT images ”, IEICE, IEICE Technical Report, pp.41-46, MI2001-41 (2001-09)” "Wakita et al.," Intelligent CAD for diffuse lung disease ", Ministry of Education, Culture, Sports, Science and Technology, Grant-in-Aid for Scientific Research on Specific Areas," Intelligent Diagnosis Support for Multidimensional Medical Images ", Proceedings of the 4th Symposium, pp. 45-54 , 2007, Consolidation, Ground-Glass Opacity (GGO), Crazy-Paving, Honeycomb, Emphysema, Granular Shadow and other diffuse lung disease detection methods, “Wakita et al.,“ Multi-temporal abdomen Liver cancer detection based on interphase concentration feature measurement of X-ray CT images ", Assisted by the computer-aided diagnostic imaging society, Vol.10, No.1, Mar 2007, “Detection method of liver cancer”, “Intelligent CAD based on understanding of normal structure”, Ministry of Education, Culture, Sports, Science and Technology Hepatocellular carcinoma, hepatic cyst, hepatic blood vessel type, and bleeding in the liver region as shown in Gold Specific Area Research "Support for Intelligent Diagnosis of Multidimensional Medical Images", 4th Symposium Proceedings pp.55-60, 2007 " A detection method or the like can be used.

  The abnormal area automatically extracted by the abnormal area extraction unit 13 may be displayed on the display 3, and the abnormal area may be corrected by the user using the input device 4.

  The partial excision region extraction unit 14 receives the abnormal region extracted by the abnormal region extraction unit 13 and extracts a partial excision region based on the abnormal region. Specifically, the partial resection region extraction unit 14 first sets a minimum inclusion body that includes an abnormal region. FIG. 2 is a diagram illustrating an example of the inclusion body 52 set based on the abnormal region 51 by the partial excision region extraction unit 14. In the present embodiment, as shown in FIG. 2, a circumscribed sphere having a minimum radius that encloses the abnormal region 51 is calculated by a known method, and the calculated sphere is set as an inclusion body 52. The set coordinates and radius r of the center 52a of the inclusion body 52 are stored in the memory.

  Next, the partial resection region extraction unit 14 extracts a region belonging to the inside of the paraboloid circumscribing the inclusion body 52 as the partial resection region 54. FIG. 3 is a diagram illustrating an example of the partial excision region 54 extracted by the partial excision region extraction unit 14. In the present embodiment, as shown in FIG. 3, a rotation paraboloid 53 that encloses a sphere that is the inclusion body 52 is calculated, and a region that belongs to the inside of the rotation paraboloid is extracted as a partially excised region 54.

The partial excision region extraction unit 14 calculates a plurality of rotational paraboloid surfaces 53 circumscribing the inclusion body 52, and is a region belonging to the inside of each of the rotational paraboloid surfaces, and a region having a minimum volume and a cut surface. The area with the smallest surface area is calculated as the partially excised area 54. Here, a plurality of rotation parabolas circumscribing the inclusion body 52 are calculated by changing the constant a in the following equation (1), and each rotation paraboloid belongs to the inside of the rotation paraboloid. Calculate the volume and surface area of the region. Then, a partial excision region 54 in which the volume of the region belonging to the inside of the calculated plurality of paraboloids 53 is minimized and a partial excision region 54 in which the surface area of the cut surface is minimized are calculated. Further, data specifying the paraboloid such as the axis 53a of the paraboloid corresponding to the determined partial resection area 54 and the intersection 53b between the axis 53a and the target tissue surface is stored in the memory. In equation (1), the center 52a of the sphere that is the inclusion body 52 is the origin of the paraboloid of revolution, the radius of the sphere that is the inclusion body is r, and the target tissue is the center 52a of the inclusion body 52 that is the origin. The direction toward the surface is the y-axis positive direction.

  Further, in the present embodiment, the region belonging to the inside of the rotating paraboloid 53 circumscribing the inclusion body 52 is defined as the partially excised region 54, but the present invention is not limited to this, for example, a spheroid parabolic surface circumscribing the inclusion body 52. A region belonging to the inner side of the inner surface of the inner body 52 or a region belonging to the inner side of the conical surface 53A (see FIG. 3) circumscribing the inclusion body 52 may be used as the partial cut region.

  In addition, it is good also as a structure which can display the partial excision area | region extracted automatically by the partial excision area extraction part 14 on the display 3, and can correct a partial excision area | region using the input device 4 by the user.

  The dominant region extraction unit 15 receives the organ region extracted by the target tissue region extraction unit 12 and the abnormal region extracted by the abnormal region extraction unit 13, and extracts a tubular structure region from the organ region. A region including a part of the region of the tubular structure related to the abnormal region is extracted as a dominant region.

  The dominant region extraction unit 15 first performs a blood vessel region extraction process and a tree structure detection process on the input liver region 5 as shown in FIG. Specifically, the linear structure is searched by calculating eigenvalues of a 3 × 3 Hessian matrix for each local region in the liver region 5. In the region including the linear structure, one of the three eigenvalues of the Hessian matrix is a value close to 0, and the other two are relatively large values. The eigenvector corresponding to the eigenvalue whose value is close to 0 indicates the principal axis direction of the linear structure. Using this relationship, the dominating region extraction unit 15 determines the likelihood of a linear structure based on the eigenvalues of the Hessian matrix for each local region, and determines the central point of the local region where the linear structure is identified. Detect as candidate points.

  Then, the candidate points detected by the search are connected based on a predetermined algorithm. Thereby, a tree structure composed of candidate points and blood vessel branches (edges) connecting the candidate points is constructed. The coordinate information of the detected candidate points and vector information indicating the direction of the blood vessel branch are stored in the memory together with the candidate point and the identifier of the blood vessel branch. Subsequently, for each detected candidate point, the outline of the blood vessel (outer wall of the blood vessel) is identified in a cross section perpendicular to the blood vessel path based on the value of the surrounding voxel (CT value). The shape is identified using a known segmentation technique represented by Graph-Cuts. Through the above processing, the blood vessel region 6 having a tubular structure as shown in FIG. 4 is extracted, and information necessary for specifying the extracted blood vessel region 6 is generated and stored in the memory.

  Then, as shown in FIG. 4, the dominant region extraction unit 15 extracts an organ portion including a partial region 55 of the blood vessel region 6 related to the abnormal region and the abnormal region 51 as the dominant region 56. Specifically, the dominant region extraction unit 15 first sets a partial region 55 of the blood vessel region 6 related to the abnormal region. As a method for setting a part of the region 55, various well-known methods can be employed. In this embodiment, the blood vessel region 6 is displayed on the display, and the blood vessel region by the user's operation is displayed by an input device such as a mouse. The designation of the position 55a is accepted, and an area extending in the direction of the abnormal area from the accepted designated position 55a is set as a partial area 55. The partial region 55 of the blood vessel region 6 may be automatically set by a known technique as described in JP-A-2001-283191.

  Then, the dominating region extraction unit 15 determines the dominating region 56 based on the partial region 55 of the blood vessel region 6 related to the abnormal region set as described above. As a specific method for determining the dominant region, for example, when the target tissue is the liver, blood vessels in the liver region are extracted, and a region other than the blood vessels in the liver region (Voronoi diagram) is used ( A method of specifying the control region of each blood vessel as a liver segment by specifying which blood vessel control region belongs to the liver parenchyma (Japanese Patent Laid-Open No. 2003-033349, “R Beichel et al.,” Liver segment approximation in CT data for surgical resection planning ”, Medical Imaging 2004: Image Processing. Edited by Fitzpatrick, J. Michael; Sonka, Milan, 2004, Proceedings of the SPIE, Volume 5370, pp. 1435-1446”) Etc. can be used to acquire the control region.

  For example, when the target tissue is the lung, the control region can be determined by applying the following method. First, a set of pixels in the bronchial region is extracted by the region expansion method, thinning processing is performed on the extracted bronchial region, and each pixel on the thin line is determined based on the connection relationship of the thin lines representing the obtained bronchi. Tree structure data representing the bronchi is obtained by classifying it into end points, edges (sides), and bifurcation points (for details, see “Daisuke Kobayashi, 5 others”, “Building a branch-based tree structure model for blood vessel shape description” Trial ", [online], March 9, 2005, RIKEN, RIKEN symposium, Quantification of body shape information and database construction research, pp.84-92, [Search January 6, 2010]," Internet < URL: http://www.comp-bio.riken.jp/keijyo/products/2005_1_files/kobayashi_print.pdf>, “Sho Nakamura, 4 others,” “Pulmonary artery and lung from chest X-ray CT images by tree structure analysis” "Automatic classification of veins", IEICE technical report. MI, Medical Images, Japan, Inc. The Institute of Electronics, Information and Communication Engineers, January 21, 2006, Vol. 105, No. 580, pp. 105-108, [Search November 20, 2009], Internet <URL: http: //www.murase. nuie.nagoya-u.ac.jp/~ide/res/paper/J05-kenkyukai-snaka-1.pdf> etc.) Then, the obtained bronchial structure is used as a base point set to perform three-dimensional Voronoi division, Find which of the bronchus that make up the bronchial structure each pixel in the lung field area is closest to, ie, which bronchus is controlled by each pixel in the lung field area. (For details, see “Akira Hirano and five others,“ Quantification of lung lobe contraction in chest CT images using 3D Voronoi segmentation and application to tumor shadow discrimination ”, [Online], July 2001, Proceedings of the 20th Annual Meeting of the Japan Society for Medical Image Engineering, pp.315-316, [searched November 20, 2009] ", Internet URL: http://mase.itc.nagoya-u.ac.jp/~hirano/Papers/JAMIT2001.pdf> etc. As another example, a technique for extracting lung fields is disclosed in Japanese Unexamined Patent Publication No. 137230, Japanese Unexamined Patent Application Publication No. 2008-253293, and Japanese Unexamined Patent Application Publication Nos. 2001-283191 and 2002-345807 can be applied to techniques for extracting the liver.

  Any other known method can be used as long as it can extract the dominant region.

  Alternatively, the blood vessel region 6 and the control region 56 automatically extracted by the control region extraction unit 15 may be displayed on the display 3 and may be corrected by the user using the input device 4.

  The ablation method acquisition unit 16 acquires a plurality of ablation methods in which ablation areas satisfying the ablation area determination conditions are set for the abnormal area based on a plurality of ablation area determination conditions set in advance. The ablation area determination condition is a condition used when determining the ablation area for each abnormal area. For example, the condition that the total volume of the ablation areas set for each abnormal area is the smallest, There is a condition that the sum of the surface areas of the cut surfaces of the ablation region set for the region is the smallest.

  Then, the ablation method acquisition unit 16 acquires one of the ablation methods by setting an ablation region for each abnormal region so as to satisfy the ablation region determination condition described above.

  Specifically, for example, when the ablation area determination condition is a condition that the total volume of the ablation area is minimized, the partial ablation area and the control area described above are temporarily set for each abnormal area. A plurality of method candidates are obtained, the sum of the volume of the excision region is calculated for each of the excision method candidates, and the excision method candidate having the smallest sum of the volumes of the excision region is acquired as an excision method that satisfies the excision region determination condition. At this time, as the partial ablation region temporarily set for the abnormal region, the partial ablation region having the minimum volume may be used.

  Also, for example, when the ablation area determination condition is a condition that the total surface area of the cut surface of the ablation area is minimized, a partial ablation area and a control area are temporarily set for each abnormal area as described above. Multiple ablation method candidates, calculate the sum of the cut surface area of the ablation area for each ablation method candidate, and determine the ablation method candidate with the smallest sum of the cut surface area of the ablation area as the ablation area determination condition Get as a resection method that satisfies. In addition, what is necessary is just to use the partial excision area | region where the surface area of the cut surface mentioned above becomes the minimum as the partial excision area | region temporarily set with respect to an abnormal area | region at this time.

  In addition, the ablation region determination condition can be arbitrarily set by the user using the input device 4. In addition to the volume and surface area conditions of the cut surface as described above, for example, the surface of the liver in the abnormal region You may make it use the distance from. As one of the conditions for determining the resection area, when the distance from the liver surface of the abnormal area is used, for example, when the distance from the liver surface of the abnormal area is equal to or less than a predetermined threshold, Sets the above-mentioned partial excision region, and if the distance from the liver surface of the abnormal region is larger than a predetermined threshold, one of the excision methods may be acquired by setting the above-mentioned dominant region . In addition to the above distance, whether the region is a partially excised region or a dominant region may be set including conditions such as the volume and radius of the abnormal region.

  Note that the distance from the abnormal area to the liver surface may be obtained, for example, by calculating the distance between a point on the liver surface and the center of gravity of the abnormal area. A point with the shortest distance from the surface may be adopted, or a point specified by the user using the input device 4 and designated or corrected may be accepted.

  Further, as one of the resection area determination conditions, for example, a condition that the curvature of the cut surface of the resection area is minimized may be used. Specifically, by setting a partial ablation region and a dominant region for the abnormal region, calculating the curvature of each cut surface, and setting the ablation region having a smaller curvature for the abnormal region What is necessary is just to acquire one of the excision methods. As a method of calculating the curvature of the ablation region, for example, the curvature is calculated for each of a plurality of points set over the entire ablation region, and the average value or median value of the plurality of curvatures is obtained as the curvature of the ablation region. Alternatively, the maximum value of a plurality of curvatures may be obtained as the curvature of the cut region.

  The plurality of excision methods acquired by the excision method acquisition unit 16 are output to the display control unit 17.

  The display control unit 17 causes the display 3 to display an image in which images representing each excision method acquired by the excision method acquisition unit 16 are superimposed on the three-dimensional morphological image 7 of the liver. As an image representing the excision method, for example, an image obtained by coloring the excision region or a translucent image may be used to generate an image that can be distinguished from the region other than the excision region. Further, the abnormal region 51, the inclusion body 52, the blood vessel region 6, a part of the blood vessel region 55, and the like may be displayed on the display 3 so that they can be identified, for example, by color-coded display.

  FIG. 5 shows an example of the excision method displayed on the display 3 by the display control unit 17. FIG. 5 shows the ablation method 1 acquired based on the condition that the total volume of the ablation areas set for each abnormal area is the smallest, and the cut surface of the ablation area set for each abnormal area The resection method 2 acquired based on the conditions that the sum total of the surface area of the particle | grains becomes the smallest is shown.

  Specifically, in the excision method 1, when the excision region determination condition is a condition that the total sum of the excision regions is minimized, the dominant region is set as the excision region with respect to the abnormal region 51a, and the abnormal region 51b FIG. 6 shows a resection method when a partial resection region is set as a resection region. In the excision method 2, when the excision region determination condition is a condition that the total surface area of the cut surfaces of the excision region is minimized, a partial excision region is set as the excision region with respect to the abnormal region 51a. The ablation method when the dominant area is set as the ablation area for 51b is shown. Note that FIG. 5 shows an example of display forms of a plurality of excision methods, and the volume and surface area of the excision area shown in FIG. 5 do not show accurate ones.

  FIG. 5 shows an example of a plurality of excision methods. Depending on the position of the abnormal region, for example, a partial excision region is set as an excision region for one abnormal region 51a, and for the other abnormal region 51b. In some cases, the ablation method has a dominant region set as the ablation region, or the ablation method has a partial ablation region set for both the abnormal region 51a and the abnormal region 51b, or the abnormal region 51a. In some cases, the ablation method has a dominant region set for both the abnormal region 51b and the abnormal region 51b.

  The storage device 2 outputs, as a three-dimensional morphological image 7, volume data reconstructed based on slice data output from a CT device or an MRI device, an MS (Multi Slice) CT device, or a cone beam CT device. Volume data and the like are stored.

  Further, the storage device 2 stores, as the three-dimensional functional image 8, a SPECT image output from the SPECT device, a functional image generated by analyzing volume data output from the MSCT device, and the like.

  FIG. 6 is a flowchart for explaining the operation of the surgery support system of the present embodiment. Hereinafter, description will be given with reference to this flowchart.

  First, in the surgery support system of the present embodiment, a selection menu is displayed on the display 3, and when it is detected that the surgery support function according to the present embodiment is selected in the selection menu, a list of subject IDs is displayed. It is displayed on the display 3. Then, when the ID of the subject to be operated is selected from the list of subject IDs displayed on the display 3 and the selection operation by the user is detected by the surgery support device 1, A medical image related to the selected subject is acquired by the medical image acquisition unit 11 (S10).

  Next, the three-dimensional morphological image 7 acquired by the medical image acquisition unit 11 is input to the target tissue region extraction unit 12, and the target tissue region extraction unit 12 extracts a liver region from the input three-dimensional morphological image 7. (S12).

  Next, the liver region extracted by the target tissue region extraction unit 12 is input to the abnormal region extraction unit 13, and the abnormal region extraction unit 13 extracts the abnormal region from the liver region (S14). In the present embodiment, a three-dimensional morphological image 7 of the liver region is displayed on the display 3, and an abnormal region included in the displayed liver region is designated by the user using the input device 4. The abnormal area extraction unit 13 detects the user's designation operation and extracts the detected area as an abnormal area.

  Then, the abnormal region extracted by the abnormal region extraction unit 13 is input to the partial excision region extraction unit 14, and the partial excision region extraction unit 14 minimizes the volume based on the input abnormal region as described above. A partially excised region and a partially excised region where the surface area of the cut surface is minimized are extracted (S16). On the other hand, the abnormal region extracted by the abnormal region extraction unit 13 is also input to the control region extraction unit 15, and the control region extraction unit 15 parallels the extraction of the partial excision region based on the input abnormal region. Then, a dominant region is extracted (S18).

  Next, the partial excision region extracted for each abnormal region by the partial excision region extraction unit 14 and the control region extracted for each abnormal region by the control region extraction unit 15 are input to the excision method acquisition unit 16. Is done.

  Then, as described above, the excision method acquisition unit 16 sets each condition so as to satisfy this condition based on the ablation area determination condition that the total volume of the ablation areas set for each abnormal area is the smallest, for example. One of the excision methods is acquired by setting a partial excision region or a dominant region for the abnormal region. In addition, based on the ablation area determination condition that the total surface area of the cut surface of the ablation area set for each abnormal area is the smallest, the partial ablation area or the control is controlled for each abnormal area so as to satisfy this condition. An area is set and one of the excision methods is acquired (S20).

  Then, the plurality of excision methods acquired by the excision method acquisition unit 16 are output to the display control unit 17, and the display control unit 17 performs the liver 3 as shown in FIG. 7 based on the input plural excision methods. An image representing each excision method superimposed on the dimensional form image 7 is displayed on the display 3 (S22).

  According to the surgery support system of the above embodiment, based on a plurality of resection area determination conditions, a plurality of ablation methods in which ablation areas that satisfy each ablation area determination condition are set for an abnormal area are acquired, and the acquired plurality Therefore, a more appropriate excision method that takes into account the position of the abnormal region in the target organ and the positional relationship between the target organ and surrounding organs from among the presented excision methods. The doctor can choose.

  In the above embodiment, a plurality of excision methods are displayed as a list as shown in FIG. 5, but the present invention is not limited to this, and each excision method may be switched and displayed. In this case, for example, the input device 4 may receive a switching instruction from the user, and the excision method may be switched according to the switching instruction.

  In the above embodiment, two excision methods are acquired and displayed as shown in FIG. 5, but the user corrects further from the excision method displayed in this way depending on the position of the abnormal region, for example. There is also a case. Therefore, it may be configured such that the user can make corrections using the input device 4 or the like. For example, as shown in FIG. 7, the partial ablation region 54a of the ablation method 2 acquired on the condition of the surface area of the cut surface of the ablation region is changed to the dominant region 56a of the ablation method 1 acquired on the condition of the volume of the ablation region. When the user wants to change, the user selects the control region 56a using the input device 4, and moves the selected control region 56a to the position of the partial resection region 54a of the resection method 2 to thereby remove the resection region of the abnormal region 51a. May be corrected to the dominant region. Note that the method of correcting the ablation region is not limited to this, and other methods may be employed.

  FIG. 5 shows images representing two excision methods. As described above, for example, one of the excision methods is acquired using the distance from the surface of the liver of the abnormal region as the excision region determination condition, As shown in FIG. 8, images representing the three excision methods may be displayed.

  Further, as shown in FIG. 5, images representing a plurality of excision methods may be displayed, and evaluation values for the respective excision methods as shown in FIG. 9 may be displayed as a list. Here, the evaluation value is a value calculated when determining each cutting region determination condition, and is calculated by the cutting method acquisition unit 16. For example, when the total volume of the ablation region and the total surface area of the cut surface of the ablation region are set as the ablation region determination condition as in the above embodiment, the display control unit 17 performs the ablation method as shown in FIG. The sum of the volume of the resection area in the case of 1 is displayed as an evaluation value a, the sum of the surface areas of the cut surface of the resection area is displayed as an evaluation value c, and the sum of the volume of the resection area in the case of the resection method 2 is an evaluation value It displays as b, and displays the sum total of the surface area of the cut surface of an excision area as evaluation value d.

  By displaying the evaluation value in this way, it is possible to present one of the determination materials when the user determines which excision method is optimal. Further, the user can determine whether or not the currently determined excision method is corrected by referring to the evaluation value. When the excision method acquired by the excision method acquisition unit 16 is corrected as described above, it is preferable to update the evaluation value based on the corrected excision method.

  In FIG. 9, the total volume of the ablation area and the total surface area of the cut surface of the ablation area for each ablation method are displayed. Further, for a plurality of ablation areas set in each ablation method, You may make it list-display the evaluation value, such as the kind of excision area | region, and a volume and the surface area of a cut surface. FIG. 10 is a list display of the types (dominant region or partial ablation region), volume, and surface area of the cut surface of the two ablation regions 1 and 2 set in the ablation method 1 in FIGS. It is what. Such an evaluation value for each excision region may be displayed together with the list shown in FIG. 9, or it may be selected according to each excision method shown in FIG. 9 and displayed according to the selection. Good.

  In addition, as shown in FIG. 11, the types of excision regions for a plurality of excision regions set in each excision method and the evaluation values such as the volume and the surface area of the cut surface may be displayed as one list. Good. Further, instead of displaying the excision method as a three-dimensional morphological image as shown in FIG. 5, only a list as shown in FIG. 11 may be displayed.

  Moreover, in the said embodiment, when acquiring the excision method, it was made to acquire the excision method based on one evaluation value, such as a volume, a surface area, and a distance, However, It is not restricted to this, In combination of a some evaluation value Based on this, the excision method may be acquired. For example, an excision method may be acquired in which the depth of each excision region from the liver surface is equal to or less than a predetermined threshold and the condition that the total sum of the volumes of each excision region is minimized.

  In the above embodiment, the ablation method is acquired using the morphological features such as the volume of the ablation region, the surface area of the cut surface, and the distance from the liver surface of the abnormal region as the ablation method determination condition. However, the excision method may be acquired using a functional feature amount as the excision method determination condition.

  Specifically, first, both the three-dimensional morphological image 7 and the three-dimensional functional image 8 are acquired by the medical image acquisition unit 11, and the excision method acquisition unit 16 acquires the three-dimensional morphological image 7 and the three-dimensional functional image 8. Alignment processing is performed between them. Hereinafter, the liver will be described as an example. Using known rigid or non-rigid registration, the liver region is aligned between the three-dimensional morphological image 7 and the three-dimensional functional image 8, and the moving direction and moving amount of each pixel between these images The coordinate value of the liver region extracted from the three-dimensional functional image 8 is converted using the moving direction and moving amount. Thereby, those images can each represent information at the same position of the liver region at the same coordinate value.

  Then, the excision method acquisition unit 16 performs, for example, each abnormal region so as to satisfy this condition based on the excision region determination condition that the sum of the functional levels of the excision region set for each abnormal region is the largest. One of the ablation methods is acquired by setting a partial ablation region or a dominant region. Specifically, when a partial ablation region and a dominant region are set as ablation regions for each abnormal region, the one with the larger sum of the functional levels in the set ablation region is set as the final ablation region. get. Even when the excision method is acquired based on the function level as described above, the function level may be displayed as an evaluation value together with the excision method.

  Moreover, in the said embodiment, although one inclusion body was set with respect to one abnormal area | region, the partial excision area | region was set, but as shown in FIG. 12, as shown in FIG. 13, when any one of the plurality of abnormal areas 51 extracted within the predetermined distance R is located within the predetermined distance R, all of the plurality of abnormal areas 51 positioned within the predetermined distance R One minimum inclusion body to be included may be set. When an inclusion body including a plurality of abnormal regions is set as described above, one partial excision having a surface area smaller than the sum of the surface areas of the cut surfaces when the partial excision regions are determined for the individual abnormal regions 51. The area can be calculated.

  As a modified example of the dominant region extraction process of the above embodiment, for example, a three-dimensional functional image 8 representing the functional level at each position of the organ is further acquired by the medical image acquisition unit 11, and the dominant region extraction unit 15 The dominant region may be extracted using the three-dimensional functional image 8. FIG. 13 is a diagram illustrating a modified example of the dominant region extraction process of the present embodiment. Here, in order to facilitate understanding, the case where the two blood vessels A and B are parallel blood vessels of the same diameter is illustrated.

  The dominant region extraction unit 15 first performs alignment processing between the three-dimensional form image 7 and the three-dimensional functional image 8. This alignment process is as described above.

Subsequently, the dominant region extraction unit 15 determines a region (dominant region) that is controlled by the blood vessel. Specifically, in each path an to bn (n = 1, 2, 3,...) Perpendicular to the main axis direction of the blood vessel A and the blood vessel B, the boundary between the dominant regions of the blood vessel A and the blood vessel B The point cn is obtained by the following equation (2). Note that α in Equation (2) is a function level at each position on the above-described route. The plurality of boundary points obtained by this processing represent the boundary line C of the dominant region of the blood vessels A and B.

  In FIG. 13, the function level α at each position in the region 61 is 1, the function level α in the region 62 is 1/3, the function level α in the region 63 is 2, and the function at each position on the path is A specific example of the boundary point cn determined in three routes an to bn (n = 1, 2, 3) having different level distributions is shown. As shown in FIG. 13, the distance from the center line of the blood vessel to the boundary of the dominant region depends on the distribution of function levels on the path. That is, the distance from the center line of each blood vessel to the boundary of the dominant region increases when the function level α on the path is low, and decreases when the function level α on the path is high.

  By setting the dominant region based on the organ function level in this way, when the dominant region is determined as the ablation region, a more appropriate ablation region can be automatically set.

  In the above embodiment, for each excision method, the volume of the non-excision area excluding the excision area is calculated, and a warning is given when the volume of the non-excision area is 1/3 of the entire liver volume. Also good. In general, since it is preferable to set the remaining portion of the liver after resection to be 1/3 or more of the entire liver volume, a guideline for setting the volume of the resection area in an appropriate range can be presented, and the user can set an appropriate resection area Can support.

  Moreover, in the said embodiment, although the surgery assistance apparatus 1 was what each program was integrated in one computer, by each program being distributed and incorporated in a plurality of computers, the surgery assistance apparatus 1 and A surgery support system that realizes an equivalent function may be constructed.

  In the above embodiment, the case where a plurality of excision methods are displayed and output by the surgery support apparatus 1 has been described. However, the output method is not limited to this, and for example, print output, data output (CD-R, DVD, etc.) Recording on media or transfer via a network).

  Moreover, although the said embodiment illustrated the process in case the target tissue is a liver and the tubular structure at the time of determining a control area | region is a blood vessel, as a target tissue in this invention, not only a liver but a lung etc. It can also be applied to support surgery for other organs.

DESCRIPTION OF SYMBOLS 1 Surgery support apparatus 2 Memory | storage device 3 Display 4 Input device 11 Medical image acquisition part 12 Target tissue area | region extraction part 13 Abnormal area extraction part 14 Partial excision area extraction part 15 Control area extraction part 16 Ablation method acquisition part 17 Display control part

Claims (14)

Medical image acquisition means for acquiring a three-dimensional medical image of a subject;
Target tissue region extraction means for extracting a target tissue region from the three-dimensional medical image acquired by the medical image acquisition means;
An abnormal region extracting unit that extracts an abnormal region from the target tissue region extracted by the target tissue region extracting unit;
Based on a plurality of ablation region determination conditions, ablation method acquisition means for acquiring a plurality of ablation methods that set ablation regions that satisfy each ablation region determination condition for the abnormal region;
An operation support apparatus comprising: an excision method presenting unit that presents a plurality of excision methods acquired by the excision method acquiring unit. A partial ablation region that sets a minimum inclusion body that includes the abnormal region extracted by the abnormal region extraction means and extracts a region including the set inclusion body and a point on the surface of the target tissue as a partial ablation region Extraction means;
A control region extracting means for extracting a region of a tubular structure from the three-dimensional medical image and extracting a region including a part of the region of the tubular structure related to the abnormal region and the abnormal region as a control region;
The surgery support apparatus according to claim 1, wherein the excision method acquisition means acquires the excision method by setting either the partial excision region or the dominant region with respect to the abnormal region. .   The surgery support apparatus according to claim 2, wherein the ablation area determination condition for setting either the partial ablation area or the dominant area is a distance between the abnormal area and the surface of the target tissue.   The operation support apparatus according to any one of claims 1 to 3, wherein one of the plurality of resection region determination conditions is a volume of the resection region.   The surgery support apparatus according to any one of claims 1 to 4, wherein one of the plurality of resection region determination conditions is a surface area of a cut surface of the resection region. The excision method acquisition means receives a correction instruction for the acquired excision method,
The surgery support device according to any one of claims 1 to 5, wherein the excision method presenting unit presents a modified excision method based on the correction instruction. The excision method acquisition means receives an instruction to move to another excision method of a predetermined excision region in one of the acquired excision methods,
The surgery support apparatus according to claim 6, wherein the excision method presenting unit presents an excision method after moving the predetermined excision region based on the movement instruction. The excision method obtaining means obtains an evaluation value corresponding to each excision region determination condition,
The surgery support apparatus according to any one of claims 1 to 7, wherein the excision method presenting unit presents an evaluation value acquired by the excision method acquiring unit.   The surgery support apparatus according to claim 8, wherein the excision method presenting unit presents a list in which the excision method corresponding to each of the excision region determination conditions is associated with the evaluation value. The medical image acquisition means acquires a three-dimensional functional image as the three-dimensional medical image;
The cutting method acquisition unit acquires one of the plurality of cutting methods based on a cutting region determination condition based on a function level in the three-dimensional functional image. The surgical operation support device according to any one of 9.   The surgery support apparatus according to any one of claims 1 to 10, wherein the target tissue is a liver.   The operation support apparatus according to claim 1, wherein the target tissue is a lung. Acquiring a 3D medical image of the subject,
Extracting a region of the target tissue from the acquired three-dimensional medical image;
An abnormal region is extracted from the extracted target tissue region,
Based on a plurality of ablation region determination conditions, obtain a plurality of ablation methods set ablation regions that satisfy each ablation region determination condition for the abnormal region,
Presenting the plurality of obtained excision methods. Computer
Medical image acquisition means for acquiring a three-dimensional medical image of a subject;
Target area extraction means for extracting a target tissue area from the three-dimensional medical image acquired by the medical image acquisition means;
An abnormal area extracting means for extracting an abnormal area from the target tissue area extracted by the target tissue extracting means;
Based on a plurality of ablation region determination conditions, ablation method acquisition means for acquiring a plurality of ablation methods that set ablation regions that satisfy each ablation region determination condition for the abnormal region;
An operation support program that functions as an ablation method presentation unit that presents a plurality of ablation methods acquired by the ablation method acquisition unit.

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